-
Notifications
You must be signed in to change notification settings - Fork 0
/
DL_test.py
125 lines (108 loc) · 4.67 KB
/
DL_test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
import os
import numpy as np
import pandas as pd
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
import matplotlib.pyplot as plt
from sklearn.preprocessing import label_binarize
from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc, precision_score, recall_score, \
f1_score, roc_auc_score
### Load the model structure
from model import *
### Load the model parameters
# model = torch.load("model_save/mlp/mlp_lr0.001_withdrop/model_epoch250_gpu.pth", map_location=torch.device('cpu')) #84.50%
# model = torch.load("model_save/cnn/cnn-lr0.01-withdrop/model_epoch110_gpu.pth", map_location=torch.device('cpu')) #91%
model = torch.load("model_save/cnn/cnn-lr0.001-withdrop-epoch400/model_epoch400_gpu.pth", map_location=torch.device('cpu')) #93%
### Define Dataset, transform and load data.
class MyData(Dataset):
def __init__(self, img_dir, label_dir):
self.img_dir = img_dir
self.img_path = os.listdir(self.img_dir)
self.label_dir = label_dir
self.label_path = pd.read_csv(self.label_dir)
self.Y_N = self.label_path['label']
self.Y_N[self.Y_N == 'no_tumor'] = 0
self.Y_N[self.Y_N == 'meningioma_tumor'] = 1
self.Y_N[self.Y_N == 'glioma_tumor'] = 2
self.Y_N[self.Y_N == 'pituitary_tumor'] = 3
def __getitem__(self, idx):
img_name = self.img_path[idx]
img_item_path = os.path.join(self.img_dir, img_name)
img_read = Image.open(img_item_path)
data_transform = transforms.Compose([
transforms.Grayscale(num_output_channels=1),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5])
])
img = data_transform(img_read)
label = self.Y_N[idx]
# trans_tensor = transforms.ToTensor()
# label = trans_tensor(label_read)
return img, label
def __len__(self):
return len(self.img_path)
img_dir = "AMLS-2021_test/test/image"
label_dir = "AMLS-2021_test/test/label.csv"
dataset_test = MyData(img_dir, label_dir)
test_data_size = len(dataset_test)
print("The length of testing set is：{}".format(test_data_size))
### Load the data
test_dataloader = DataLoader(dataset_test, batch_size=len(dataset_test))
### The testing step begins
model.eval()
# total_test_loss = 0
total_accuracy = 0
### no grad optimation
with torch.no_grad():
for data in test_dataloader:
imgs, targets = data
outputs = model(imgs)
# loss = loss_fn(outputs, targets)
# total_test_loss += loss.item()
accuracy = (outputs.argmax(1) == targets).sum()
total_accuracy = total_accuracy + accuracy
# print("loss on entire testing set：{}".format((total_test_loss)))
softmax = nn.Softmax(dim=1)
outputs = softmax(outputs)
# print(outputs)
print("Accuracy:{}".format(total_accuracy/test_data_size))
print('micro_precision:{}'.format(precision_score(targets, outputs.argmax(1), average='micro')))
print('micro_recall:{}'.format(recall_score(targets, outputs.argmax(1), average='micro')))
print('micro_f1-score:{}'.format(f1_score(targets, outputs.argmax(1), average='micro')))
print("Confusion Matrix: ",'\n', confusion_matrix(targets, outputs.argmax(1)))
print("Classification report: ",'\n', classification_report(targets, outputs.argmax(1)))
### Draw the ROC of multiple classes
classes = ['no_tumor', 'meningioma', 'glioma', 'pituitary']
for i in range(0, 4):
fpr, tpr, thresholds = roc_curve(targets, outputs[:, i], pos_label=i)
auc_ = auc(fpr, tpr)
lw = 2
plt.plot(fpr, tpr,
lw=lw, label='ROC curve of {} (area = %0.2f)'.format(classes[i]) % auc_)
plt.legend(loc="lower right")
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Multiclass ROC of CNN')
# plt.savefig('save/roc_CNN.png')
# plt.title('Multiclass ROC of MLP')
# plt.savefig('save/roc_MLP.png')
plt.show()
### Draw the ROC globally by micro
targets_one_hot = label_binarize(targets, classes=np.arange(4))
auc_micro = roc_auc_score(targets_one_hot, outputs, average='micro')
fpr, tpr, thresholds = roc_curve(targets_one_hot.ravel(), outputs.ravel())
plt.plot(fpr, tpr, linewidth=2, label='AUC=%.3f' % auc_micro)
plt.plot([0, 1], [0, 1], 'k--')
plt.axis([0, 1.1, 0, 1.1])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.legend()
plt.title('micro ROC of CNN')
# plt.savefig('save/roc_CNN_micro.png')
# plt.title('micro ROC of MLP')
# plt.savefig('save/roc_MLP_micro.png')
plt.show()